Sentiment analysis is easy—until sarcasm enters the chat. Traditional natural language processing models often stumble when trying to decode sarcastic nuances, missing crucial contextual cues and delivering misleading results. To tackle this, we will explore a multi-modal approach that integrates facial expression analysis with textual inputs, dramatically improving accuracy in sentiment detection, particularly for sarcasm. By combining transformer-based NLP models and facial landmark detection, we create a richer, context-aware understanding of sentiment.

In this session, you'll explore how facial movements—like subtle eye rolls or eyebrow raises—can be quantified, combined with language embeddings, and processed to uncover hidden sarcastic sentiment. We'll walk through real-world datasets, demonstrate model training and evaluation, and share insights on deploying these models effectively in production. Attendees will leave with practical strategies and code examples, ready to integrate facial and textual analysis to tackle sarcasm head-on in their own NLP applications. As always, we will have live demos plenty.